Entropy based multiphase flow detection

ABSTRACT

Systems, computer-implemented methods, and non-transitory computer-readable medium having a stored computer program provide characterization of multiphase fluid flow (MPF) using approximate entropy calculation techniques to enhance measuring and monitoring of a flow regime in a segment of pipe for hydrocarbon-production operations. The systems and methods can be optimized using principal component analysis.

PRIORITY

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/012,571, filed Jun. 19, 2018. U.S. patentapplication Ser. No. 16/012,571 is a continuation of U.S. patentapplication Ser. No. 15/189,451, filed Jun. 22, 2016, which claimspriority to and the benefit of U.S. Prov. App. Ser. No. 62/182,786,filed Jun. 22, 2015, the entire disclosure of which is incorporated hereby reference.

BACKGROUND

Embodiments of the disclosure relate to systems and methods forcharacterizing multiphase fluid flow (MPF) in a pipe.

The simultaneous flow of two or more physical phases is referred to asmultiphase fluid flow (MPF). The flow behavior of MPF is more complexthan for single phase flow regime patterns. The flow regime in MPF candepend on a number of factors including, for example, the relativedensity ratio of one fluid to another, difference in viscosity betweendifferent fluids, and the velocity (slip) of each fluid. MPF can includeany combination of two or more phases including solid, liquid, and gas.For example, one MPF might include sand, oil, and natural gas. Accuratemeasurement and characterization of MPF regimes is important to optimizeproduction from hydrocarbon-producing wells and to determine thecomposition and amount of production streams.

Systems and methods have been proposed for non-intrusive measurement ofMPF parameters including, for example, flow regime, flow rate, presenceof solid content, and volume and mass ratios of individual phasesrelative to one another. Active systems include those that convey anyone of or any combination of acoustic and ultrasound frequencies throughthe flow and analyze the received acoustic responses.

Non-invasive systems and methods that utilize acoustic emissions andsignals to identify various flow regimes and the presence of solidcontent in MPF employ a variety of parameters from flow acoustic datasuch as, for example, signal amplitude, root-mean-square (RMS) value,energy, and basic frequency content. Such systems and methods typicallyapply thresholding and template matching techniques. One of the manychallenges posed by existing systems and methods includes the presenceof continuous and random background acoustic and electric noise in MPFsystems.

SUMMARY OF THE INVENTION

Applicant has recognized that there is a need for accurate and efficientmeasurement systems and methods for characterizing multiphase fluid flow(MPF). Applicant also has recognized that using approximate entropycalculations and techniques with the systems and methods of the presentdisclosure allows for accurate, real-time measurement andcharacterization of MPF. Embodiments of the present disclosure arenon-radioactive, do not impede flow in any way, and are computationallyefficient. Embodiments of the present disclosure will allownon-intrusive measurement of various MPF parameters in any one of or anycombination of a pipe, pipeline, casing, liner, and tubing, and willidentify the presence of solids, such as, for example, sand, in the MPF.Certain embodiments will enable non-intrusive, low-cost, small, accuratemeters for measurement and characterization of MPF regimes and MPFcharacteristics, which will improve monitoring, production, andreservoir management in hydrocarbon-retrieval applications.

Moreover, Applicant has recognized a statistical-based approach that canquantify short-term and long-term complexity and randomness in MPF usingapproximate entropy calculations. Different MPF flow regimes willexhibit different values of statistical randomness, especially overshorter time periods. Principal component analysis is used to optimizecertain embodiments of the present disclosure.

Having recognized deficiencies in existing systems and methods formeasuring MPF, the sources of these deficiencies, and solutions to thedeficiencies, Applicant discloses embodiments of systems,computer-implemented methods, and a non-transitory, computer-readablemedium having stored computer programs to provide passive and activesystems and methods for characterizing MPF and thereby enhance measuringand monitoring of a flow regime in a segment of pipe forhydrocarbon-production operations. Hydrocarbon-production operations canrefer to any upstream or downstream production concerning hydrocarbonsin any form, including, but not limited to, crude oil, natural gas,natural gas condensates, liquefied petroleum gas, heavy products, lightproducts, and distillates.

Embodiments of the disclosure can include a passive multiphase fluidflow (MPF) characterization system to enhance measuring and monitoringof a flow regime in a segment of pipe for hydrocarbon-productionoperations. The system includes an acoustic emission sensor disposedproximate to the segment of pipe and operable to receive an acousticemission from a MPF, the segment of pipe operable to support the MPF inhydrocarbon-production operations including at least two physicalphases, and the acoustic emission sensor operable to convert thereceived acoustic emission to an electrical signal. The system furtherincludes a processing unit, including a processor, operable to receivethe electrical signal and transform the electrical signal tocharacterize the MPF, the processing unit in communication with andcomprising non-transitory, tangible memory medium in communication withthe processor having a set of stored instructions, the set of storedinstructions being executable by the processor. The processor executesthe steps of segmenting the electrical signal into short term, mediumterm, and long term time series, and assigning positive real numbers tothe time series, the positive real numbers including larger values andsmaller values, the larger values corresponding to process randomness,and the smaller values corresponding to instances of recognizablepatterns in the electrical signal.

The processor further executes the steps of categorizing certainpositive real numbers as outlier values, calculating short term, mediumterm, and long term approximate entropy values for the MPF responsive tothe short term, medium term, and long term time series from theelectrical signal, and comparing the short term, medium term, and longterm approximate entropy values for the MPF to pre-determined shortterm, medium term, and long term approximate entropy values. Theprocessor further executes the step of determining characteristics ofthe MPF responsive to similarities between the short term, medium term,and long term approximate entropy values for the MPF and thepre-determined short term, medium term, and long term approximateentropy values. The processing unit further includes a user interfacecoupled to the processing unit, the user interface operable to acceptuser inputs to control the processing unit, and operable to display thecharacteristics of the MPF to a user.

In some embodiments, the system further comprises a database withpre-determined short term, medium term, and long term approximateentropy values for a variety of MPF flow regimes. In other embodiments,the system further comprises a preamplifier coupled to the acousticemission sensor, and operable to receive and amplify the electricalsignal from the acoustic emission sensor. In some embodiments, thesystem further comprises a band-pass signal filter coupled to thepreamplifier, the band-pass signal filter operable to receive anamplified electrical signal from the preamplifier, and further operableto remove acoustic background noise from useful MPF acoustic informationcontained within the amplified electrical signal, responsive toprogrammed cutoff frequencies in the band-pass signal filter derivedfrom an operating frequency and bandwidth of the acoustic emissionsensor.

Still in other embodiments, the system includes an analog-to-digitalconverter coupled to the band-pass signal filter, the analog-to-digitalconverter operable to receive from the band-pass signal filter theuseful MPF acoustic information, and operable to convert the useful MPFacoustic information to a digital signal. In some embodiments of thesystem, the acoustic emission sensor comprises a first acoustic emissionsensor, and the system further comprises a second acoustic emissionsensor disposed proximate to the segment of pipe and operable to receivean acoustic emission from the MPF, the second acoustic emission sensorfurther operable to convert the received acoustic emission to anelectrical signal.

In some embodiments of the system, the second acoustic emission sensoris disposed at a distance D from the first acoustic emission sensor,where the distance D is operable to allow coherent measurements of theMPF in a substantially similar state at both the first acoustic emissionsensor and the second acoustic emission sensor, and where an accuratemeasurement of flow velocity of the MPF is obtained by dividing thedistance D by a difference in time between a first time at which the MPFpasses the first acoustic emission sensor and a second time at which theMPF passes the second acoustic emission sensor.

Still in other embodiments of the system, the processing unit further isoperable to execute a set of instructions to conduct a principalcomponent analysis on the system including the steps of gatheringacoustic emission data under a variety of flow parameters in situationsin which an appropriate Reynolds number is known for the MPF for whichdata is being gathered forming time series of acoustic waveforms, andperforming a Fourier Transformation on the data, the data beingconverted into measurements of acoustic power as a function offrequency. The principal component analysis further comprises the stepsof executing a suite of measurements using a test matrix includingdifferent conditions of the MPF including at least one variable selectedfrom the group consisting of stepped values of watercut, stepped valuesof total liquid flow, and MPF regimes, and post-processing the data byapplying principal component analysis to the data to determinemeasurable frequencies relevant to determining the characteristics ofthe MPF.

Some embodiments of the system further comprise an optimized acousticemission sensor, where the optimized acoustic emission sensor isoperable to receive the frequencies determined by the principalcomponent analysis to be relevant to determining the characteristics ofthe MPF.

Further disclosed is an active multiphase fluid flow (MPF)characterization system to enhance measuring and monitoring of a flowregime in a segment of pipe for hydrocarbon-production operations. Thesystem includes an acoustic receiver disposed proximate to the segmentof pipe and operable to receive an acoustic signal transmitted through aMPF, the segment of pipe operable to support the MPF inhydrocarbon-production operations including at least two physicalphases, and the acoustic receiver operable to convert the receivedacoustic signal to an electrical signal. The system further includes anacoustic transmitter disposed proximate to the segment of pipe andoperable to convey an acoustic signal through the MPF inhydrocarbon-production operations, further operable to convey theacoustic signal such that the acoustic signal is receivable by theacoustic receiver. The system further includes a processing unit,including a processor, operable to receive the electrical signal andtransform the electrical signal to characterize the MPF.

The processing unit is in communication with and includesnon-transitory, tangible memory medium in communication with theprocessor having a set of stored instructions, the set of storedinstructions being executable by the processor and including the stepsof segmenting the electrical signal into short term, medium term, andlong term time series and assigning positive real numbers to the timeseries, the positive real numbers including larger values and smallervalues, the larger values corresponding to process randomness, and thesmaller values corresponding to instances of recognizable patterns inthe electrical signal. The set of instructions further includes thesteps of categorizing certain positive real numbers as outlier valuesand calculating short term, medium term, and long term approximateentropy values for the MPF based upon the short term, medium term, andlong term time series from the electrical signal. The set ofinstructions further includes the steps of comparing the short term,medium term, and long term approximate entropy values for the MPF topre-determined short term, medium term, and long term approximateentropy values and determining characteristics of the MPF responsive tosimilarities between the short term, medium term, and long termapproximate entropy values for the MPF and the pre-determined shortterm, medium term, and long term approximate entropy values.

The processing unit further includes a user interface coupled to theprocessing unit, the user interface operable to accept user inputs tocontrol the processing unit, and operable to display the characteristicsof the MPF to a user. In some embodiments, the active system furtherincludes a database with pre-determined short term, medium term, andlong term approximate entropy values for a variety of MPF flow regimes.In some embodiments, the system further includes a preamplifier coupledto the acoustic receiver, and operable to receive and amplify theelectrical signal from the acoustic receiver. In other embodiments, thesystem further includes a band-pass signal filter coupled to thepreamplifier, the band-pass signal filter operable to receive anamplified electrical signal from the preamplifier, and further operableto remove acoustic background noise from useful MPF acoustic informationcontained within the amplified electrical signal, responsive toprogrammed cutoff frequencies in the band-pass signal filter derivedfrom an operating frequency and bandwidth of the acoustic receiver.

In some embodiments, the system further includes an analog-to-digitalconverter coupled to the band-pass signal filter, the analog-to-digitalconverter operable to receive from the band-pass signal filter theuseful MPF acoustic information, and operable to convert the useful MPFacoustic information to a digital signal. In other embodiments, thesystem further includes an amplifier disposed proximate the acoustictransmitter and operable to receive and amplify a drive signal toprovide an amplified signal to the acoustic transmitter, where theamplifier is a high-voltage amplifier operable from about 50 volts (V)to about 100 V.

In some embodiments of the system, the acoustic receiver comprises afirst acoustic receiver, and the system further comprises a secondacoustic receiver disposed proximate to the segment of pipe and operableto receive an acoustic signal transmitted through the MPF, the secondacoustic receiver further operable to convert the received acousticsignal to an electrical signal, and a second acoustic transmitterdisposed proximate to the segment of pipe and operable to convey anacoustic signal through the MPF in hydrocarbon-production operations,further operable to convey the acoustic signal such that the acousticsignal is receivable by the second acoustic receiver.

In some embodiments of the system, the second acoustic receiver isdisposed at a distance D from the first acoustic receiver, where thedistance D is operable to allow coherent measurements of the MPF in asubstantially similar state at both the first acoustic receiver and thesecond acoustic receiver, and where an accurate measurement of flowvelocity of the MPF is obtained by dividing the distance D by adifference in time between a first time at which the MPF passes thefirst acoustic receiver and a second time at which the MPF passes thesecond acoustic receiver.

In other embodiments of the active system, the processing unit furtheris operable to execute a set of instructions to conduct a principalcomponent analysis on the system including the steps of gatheringacoustic signal data under a variety of flow parameters in situations inwhich an appropriate Reynolds number is known for the MPF for which datais being gathered, forming time series of acoustic waveforms, performinga Fourier Transformation on the data, the data being converted intomeasurements of acoustic power as a function of frequency, executing asuite of measurements using a test matrix including different conditionsof the MPF including at least one variable selected from the groupconsisting of stepped values of watercut, stepped values of total liquidflow, and MPF regimes, and post-processing the data by applyingprincipal component analysis to the data to determine measurablefrequencies relevant to determining the characteristics of the MPF.

In some embodiments of the system, the system further includes anoptimized acoustic receiver, where the optimized acoustic receiver isoperable to receive the frequencies determined by the principalcomponent analysis to be relevant to determining the characteristics ofthe MPF.

Additionally disclosed is a method for characterizing multiphase fluidflow (MPF) to enhance measuring and monitoring of a flow regime in asegment of pipe for hydrocarbon-production operations. The methodcomprises the steps of sensing an acoustic emission from a MPF, thesegment of pipe operable to support the MPF in hydrocarbon-productionoperations including at least two physical phases and converting theacoustic emission to an electrical signal. The method further includesthe steps of segmenting the electrical signal into short term, mediumterm, and long term time series, and assigning positive real numbers tothe time series, the positive real numbers including larger values andsmaller values, the larger values corresponding to process randomness,and the smaller values corresponding to instances of recognizablepatterns in the electrical signal. The method further includes the stepsof categorizing certain positive real numbers as outlier values, andcalculating short term, medium term, and long term approximate entropyvalues for the MPF responsive to the short term, medium term, and longterm time series from the electrical signal.

The method further includes the steps of comparing the short term,medium term, and long term approximate entropy values for the MPF topre-determined short term, medium term, and long term approximateentropy values and determining characteristics of the MPF responsive tosimilarities between the short term, medium term, and long termapproximate entropy values for the MPF and the pre-determined shortterm, medium term, and long term approximate entropy values.

In some embodiments, the method further comprises the step of displayingthe characteristics of the MPF on a user interface, where the userinterface is operable to graphically represent at least one flow regime.In some embodiments, the method further comprises the step ofpreamplifying the electrical signal before the step of segmenting theelectrical signal. In other embodiments, the method includes the step offiltering the electrical signal, before segmenting the electricalsignal, responsive to programmed cutoff frequencies in a band-passsignal filter derived from an operating frequency and bandwidth of anacoustic emission sensor. Still in other embodiments, the method furthercomprises the step of converting the electrical signal to a digitalsignal, before segmenting the electrical signal.

In some embodiments of the method, the step of sensing an acousticemission comprises the step of sensing a first acoustic emission, andfurther comprises the step of sensing a second acoustic emission fromthe MPF, the second acoustic emission being sensed simultaneously withand at a distance D from the first acoustic emission. In otherembodiments of the method, the method includes the step of calculatingan accurate measurement of flow velocity of the MPF in response to thedistance D and sensing the first acoustic emission and sensing thesecond acoustic emission. Still in other embodiments, the methodincludes the step of conducting a principal component analysis, wherethe principal component analysis comprises the steps of gatheringacoustic emission data under a variety of flow parameters in situationsin which an appropriate Reynolds number is known for the MPF for whichdata is being gathered and forming time series of acoustic waveforms.

The principal component analysis further comprises the steps ofperforming a Fourier Transformation on the data, the data beingconverted into measurements of acoustic power as a function offrequency, executing a suite of measurements using a test matrixincluding different conditions of the MPF including at least onevariable selected from the group consisting of stepped values ofwatercut, stepped values of total liquid flow, and multiphase flowpatterns, and post-processing the data by applying principal componentanalysis to the data to determine measurable frequencies relevant todetermining the characteristics of the MPF. In some embodiments, themethod further comprises the step of optimizing the step of sensing anacoustic emission from the MPF to receive the frequencies determined bythe principal component analysis to be relevant to determining thecharacteristics of the MPF.

Additionally disclosed is a method for characterizing multiphase fluidflow (MPF) to enhance measuring and monitoring of a flow regime in asegment of pipe for hydrocarbon-production operations. The methodcomprises the steps of transmitting an acoustic signal through a MPF,receiving an acoustic signal transmitted through the MPF, the segment ofpipe operable to support the MPF in hydrocarbon-production operationsincluding at least two physical phases, and converting the acousticsignal to an electrical signal. The method further comprises the stepsof segmenting the electrical signal into short term, medium term, andlong term time series, assigning positive real numbers to the timeseries, the positive real numbers including larger values and smallervalues, the larger values corresponding to process randomness, and thesmaller values corresponding to instances of recognizable patterns inthe electrical signal, and categorizing certain positive real numbers asoutlier values.

The method further includes the steps of calculating short term, mediumterm, and long term approximate entropy values for the MPF responsive tothe short term, medium term, and long term time series from theelectrical signal, comparing the short term, medium term, and long termapproximate entropy values for the MPF to pre-determined short term,medium term, and long term approximate entropy values, and determiningcharacteristics of the MPF responsive to similarities between the shortterm, medium term, and long term approximate entropy values for the MPFand the pre-determined short term, medium term, and long termapproximate entropy values.

In some embodiments, the method further comprises the step of displayingthe characteristics of the MPF on a user interface, where the userinterface is operable to graphically represent at least one flow regime.In other embodiments, the method further comprises the step ofpreamplifying the electrical signal before the step of segmenting theelectrical signal. Still in other embodiments, the method furthercomprises the step of filtering the electrical signal, before segmentingthe electrical signal, responsive to programmed cutoff frequencies in aband-pass signal filter derived from an operating frequency andbandwidth of an acoustic receiver. In some embodiments, the methodfurther comprises the step of converting the electrical signal to adigital signal, before segmenting the electrical signal.

In some embodiments of the method, the step of receiving an acousticsignal comprises the step of receiving a first acoustic signal, andfurther comprises the step of receiving a second acoustic signaltransmitted through the MPF, the second acoustic signal being receivedsimultaneously with and at a distance D from the first acoustic signal,and where the step of transmitting an acoustic signal comprises the stepof transmitting a first acoustic signal, and further comprises the stepof transmitting a second acoustic signal through the MPF, the secondacoustic signal being conveyed simultaneously with and at a distance Dfrom the first acoustic signal.

Still in other embodiments of the method, included are the steps ofcalculating an accurate measurement of flow velocity of the MPF inresponse to the distance D and receiving the first acoustic signal andreceiving the second acoustic signal. Some embodiments include the stepof performing a principal component analysis, where the principalcomponent analysis comprises the steps of gathering acoustic signal dataunder a variety of flow parameters in situations in which an appropriateReynolds number is known for the MPF for which data is being gatheredand forming time series of acoustic waveforms. The principal componentanalysis further includes the steps of performing a FourierTransformation on the data, the data being converted into measurementsof acoustic power as a function of frequency, executing a suite ofmeasurements using a test matrix including different conditions of theMPF including at least one variable selected from the group consistingof stepped values of watercut, stepped values of total liquid flow, andmultiphase flow patterns, and post-processing the data by applyingprincipal component analysis to the data to determine measurablefrequencies relevant to determining the characteristics of the MPF.

In some embodiments, the method further includes the step of optimizingthe step of receiving an acoustic signal from the MPF to receive thefrequencies determined by the principal component analysis to berelevant to determining the characteristics of the MPF.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentdisclosure will become better understood with regard to the followingdescriptions, claims, and accompanying drawings. It is to be noted,however, that the drawings illustrate only several embodiments of thedisclosure and are therefore not to be considered limiting of thedisclosure's scope as it can admit to other equally effectiveembodiments.

FIG. 1 is a schematic diagram of a passive system according to anembodiment of the present disclosure.

FIG. 2 is a schematic diagram of a passive system according to anembodiment of the present disclosure.

FIG. 3 is a schematic diagram of an active system according to anembodiment of the present disclosure.

FIG. 4 is a schematic diagram of an active system according to anembodiment of the present disclosure.

FIG. 5 is a graphical representation of multiphase fluid flow (MPF)regimes, optionally for display on a user interface according to anembodiment of the present disclosure.

FIG. 6 is a flow chart for a method according to an embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE INVENTION

So that the manner in which the features and advantages of theembodiments of methods, systems, and a non-transitory, computer-readablemedium having stored computer programs, as well as others, which willbecome apparent, may be understood in more detail, a more particulardescription of the embodiments of methods, systems, and non-transitory,computer-readable medium having stored computer programs of the presentdisclosure briefly summarized previously may be had by reference to theembodiments thereof, which are illustrated in the appended drawings,which form a part of this specification. It is to be noted, however,that the drawings illustrate only various embodiments of the disclosureof methods, systems, and non-transitory, computer-readable medium havingstored computer programs of the present disclosure and are therefore notto be considered limiting of the embodiments of methods, systems, andnon-transitory, computer-readable medium having stored computer programsof the present disclosure's scope, as it may include other effectiveembodiments as well.

Referring now to FIG. 1, a schematic diagram of a passive acousticemissions based system 100, according to one embodiment of the presentdisclosure, is shown. In the embodiment of FIG. 1, the passive acousticemissions based system 100 includes a pipeline 102 with at least onesegment 104 operable to support multiphase fluid flow (MPF). While FIG.1 shows MPF directed in the Y1 direction, system 100 is capable ofsupporting MPF in either the Y1 or Y2 direction. Acoustic emission inMPF is defined as a physical phenomenon which occurs within and on thesurface of the MPF, and results in spontaneous release of acousticenergy in a broad frequency range of about 1 kilohertz (kHz) to about 1megahertz (MHz).

In some embodiments, the Y1 direction is the uphole direction in a wellenvironment or is the upstream direction in a pipeline environment. Insome embodiments, the Y2 direction is the downhole direction in a wellenvironment or is the downstream direction in a pipeline environment.One of ordinary skill in the art would understand that MPF can havecounter-flows and turbulence, but the flow is generally going towards oraway from the surface in a well environment and towards or away from apressure generating source, such as a pump, in a pipeline environment.

Systems and methods of the present disclosure are compatible for usewith any pipe or pipeline capable of supporting MPF, including, but notlimited to, above-ground pipelines, below-surface pipelines, under-waterpipelines, pipelines within a wellbore, and pipelines used indoors, forexample in a lab setting or pilot plant. “Pipes” and “pipelines” withina wellbore may include any one of or any combination of conduits,enclosed flow channels, coiled tubing, a drill pipe, a production line,completion casing, and drill casing.

Challenges confronting existing systems in pipelines include very lowsignal-to-noise (SNR) ratios and the stochastic nature of acousticemission signals. Moreover, other deficiencies include intrusiveness ofcommercially-available metering systems, high power consumption, use ofradioactive sources, high cost, high complexity, and large physicalsize. Because of these and other deficiencies in existing systems andmethods, most are unable to provide accurate measurements andcharacterizations of MPF in practical industrial scenarios. For example,one such environment is a downhole environment in which manyinterrelated factors affect the acoustics of MPF in a complex manner.Also, existing systems and methods do not account for acousticvariabilities and the non-stationary nature of the acoustic emission(AE) signal.

Certain characteristics of MPF are described in “Upstream MultiphaseFlow Assurance Monitoring Using Acoustic Emission,” 2012 by Al-Lababidi,S.; Mba, D.; and Addali, A. Cranfield University, UK. Acoustic emissionfrom MPF is dependent upon, in part, gas bubble formation andcavitation, regime breakage and coalescence, and interaction of variousphases within MPF. These characteristics vary for different MPF regimes,flow rates, and also for different relative amounts of liquid,gas/vapor, and solids in the MPF. In general, acoustic information isused in the embodiments of the present disclosure to characterize theMPF as well as to determine flow characteristics. For example, theproposed systems and methods can be used to identify the MPF regime(such as a slug flow), and can be used to identify individualcharacteristics of the MPF (such as slug flow frequency).

Still referring to FIG. 1, the system 100 includes an acoustic emissionsensor 106 mounted to the segment 104 of the pipeline 102. In the system100, the acoustic emission sensor 106 is a passive device used toreceive wideband acoustic emissions (usually in a range of several kHz)from the MPF. Acoustic emission sensor 106 performs one or more ofreceiving, storing, processing, and conveying MPF acoustic emissions108. The acoustic emission sensor 106 is operable to receive acousticemissions with frequencies up to about 1 MHz in order to detect usefulMPF acoustic emissions from the flow, such as the MPF acoustic emissions108. The acoustic emission sensor includes a microphone in someembodiments, and includes a commercially available acoustic emissionsensor/transducer in some embodiments.

For example, the AE1045S from Vallen Systems, headquartered in IckingGermany, with a frequency range of about 100 kHz to about 1500 kHz, canbe used. Simultaneously or alternatively a general purpose widebandacoustic emission sensor can be used, such as those commerciallyavailable from Mistras Group Ltd., headquartered in Princeton Junction,N.J. One such general purpose wideband acoustic emission sensor, forexample, is the model WSA general purpose wideband sensor, with anoperating frequency range of between about 100 kHz and about 1000 kHz.

In the embodiments of passive systems of the present disclosure, such asFIGS. 1 and 2, acoustic emission sensors, such as acoustic emissionsensor 106, are mounted exterior to a pipe or mounted within a holebored in the pipe, such that the surface of the acoustic emission sensoris in contact with the MPF. In some embodiments, a preferableconfiguration is to have the acoustic emission sensor mounted exteriorto the pipe, as the presence of the sensor interior to the pipe mayimpede or otherwise negatively impact the flow. In some instances, if anacoustic emission sensor is placed in the flow path, the interaction ofthe MPF with the sensor will generate additional undesirable acousticemissions that are not representative of the MPF.

The frequency or frequencies received by an acoustic emission sensorwill not be negatively impacted if the emission sensor is mountedexterior to the pipe. Alternatively, an acoustic emission sensor can beadvantageously mounted in a hole bored in the pipe if the MPF is notimpeded. Acoustic emission parameters that are affected by the locationof the acoustic emission sensor include the signal amplitude and thesignal-to-noise (SNR) ratio. If the sensor is mounted in a hole bored inthe pipe, the received acoustic emissions will have greater amplitudeand a better SNR.

If the sensor is mounted on the exterior of the pipe, the amplitude ofthe received acoustic emission will be weaker (as the emission will haveto travel through the pipe wall). Also, with the acoustic emissionsensor mounted exteriorly to the pipe wall, additional noise may travelthrough the pipe wall to the sensor that is not relevant to determiningMPF characteristics. However, as noted previously, mounting acousticemission sensors exterior to the pipe creates a non-invasive system. Anacoustic emission sensor can be clamped or coupled to a segment of pipeby any suitable means known in the art, such as for example any one ofor any combination of clamps, adhesives, bolts, screws, and straps.

More than one acoustic emission sensor can be used in the systems andmethods of the present disclosure, for example as shown in FIG. 2. Insome embodiments, a couplant is required to couple an acoustic emissionsensor in a suitable position with a pipeline. In the system of FIG. 1,glycerol or oil based couplant is used to properly dispose the acousticemission sensor 106 on the exterior of segment 104 to receive the MPFacoustic emissions 108. Once the acoustic emission sensor 106 receivesthe MPF acoustic emissions 108, the acoustic emission sensor 106converts the MPF acoustic emissions 108 to electrical signals. Theseelectrical signals are analog electrical signals, suited for laterconversion to digital signals, in the embodiment of FIG. 1. In otherembodiments, these electrical signals can be conveyed from the acousticemission sensor as a digital signal directly to a processing unit.

In some embodiments, when an acoustic emission sensor is mountedexterior to a pipe wall, a couplant is required to remove any air fromthe interface between the pipe wall and the sensor. Air can beintroduced due to the microstructure and surface roughness of the twocontacting surfaces of the interface between the acoustic emissionsensor and the pipe wall. One reason to avoid having air between anacoustic emission sensor and a pipe wall is that the acoustic impedanceof air is lesser (almost 5 orders of magnitude) than a pipe surface or asensor face. This low acoustic impedance allows for very littletransmission of acoustic energy from the pipe wall to the sensor withouta couplant, because most of the acoustic energy is lost. The use of acouplant can greatly improve the transmission of an acoustic emissionfrom a MPF, through the pipe wall, and to the acoustic emission sensor.A thin layer of couplant is placed between the pipe wall surface and thesensor face. Couplants with high acoustic impedance can provide betteracoustic energy transmissions and better SNR. Examples of such couplantsinclude glycerol and propylene glycol.

As shown in FIG. 1, the acoustic emission sensor 106 is coupled by aconnection 110 to a signal conversion unit 112. The connection 110 canbe a wired or wireless connection, and the MPF acoustic emissions 108received and converted to electrical signals by the acoustic emissionsensor 106 are conveyed to the signal conversion unit 112. Electricalsignals arising from the MPF acoustic emissions 108 are transferred fromthe acoustic emission sensor 106 to the signal conversion unit 112 viaany one of or any combination of a cloud-based storage medium, wiredconnection, and wireless connection.

In the embodiment of FIG. 1, the signal conversion unit 112 includes apreamplifier 114, a band-pass signal filter 116, and ananalog-to-digital converter 118. In other embodiments, the signalconversion unit includes more or fewer signal conversion units. In otherembodiments, the preamplifier, the band-pass signal filter, and theanalog-to-digital converter are not part of a single unit, such as thesignal conversion unit 112. The signal conversion unit, in someembodiments, includes hardware and a non-transitory, tangible memorymedium in communication with a processor having a stored set ofinstructions.

The MPF acoustic emissions 108 detected by the acoustic emission sensor106 are a combination of useful MPF acoustic information, which is usedto characterize the flow regime of the MPF, and random acousticbackground noise from the environment surrounding the pipeline 102. Theconverted electrical signal produced by the acoustic emission sensor 106and transferred to the preamplifier 114 by the connection 110 isamplified by the preamplifier 114 to produce an amplified electricalanalog signal. In some embodiments, more than one preamplifier is used.The amplified electrical analog signal is then conveyed by a connection120 to the band-pass signal filter 116.

Similar to the connection 110, the connection 120 can be a wired orwireless connection, and the amplified electrical signal is conveyed tothe band-pass signal filter 116. The amplified electrical signal can betransferred from the preamplifier to the band-pass signal filter via anyone of or any combination of a cloud-based storage medium, wiredconnection, and wireless connection.

After the electrical analog signal is amplified by the preamplifier 114,the signal is filtered using the band-pass signal filter 116. The cutofffrequencies of the band-pass signal filter 116 depend, in the embodimentof FIG. 1, on the operating frequency and bandwidth of the acousticemission sensor 106. In other embodiments, the cutoff frequencies of theband-pass signal filter can be adjusted by user inputs or adjustedaccording to MPF flow conditions, composition of the MPF, or otherenvironmental conditions. The band-pass signal filter 116 filters out orremoves certain undesirable frequencies that are not useful forcharacterizing the MPF regime, such as noise caused by the environmentsurrounding the pipeline 102.

In some embodiments, the band-pass signal filter can be used to removeany unwanted noise signal and improve the SNR. For example, in activesystem configurations, such as those shown in FIGS. 3 and 4 anddescribed as follows, when an acoustic signal of fixed frequency istransmitted through the MPF, a band-pass filter is used to limit thereceived signal to the known transmitted frequency or frequencies andremove all other frequencies from the signal. Also for passiveconfigurations of the system, such as those of FIGS. 1 and 2, therequired frequencies of interest for acoustic emissions from the MPFmight be in a certain frequency range for a certain application, andremaining, irrelevant frequencies are removed. These frequency rangesare determined in the lab by experimentation. Also for passiveconfigurations, such as FIGS. 1 and 2, acoustic emission frequenciesless than about 20 kHz are usually removed.

In some embodiments, an off-the-shelf integrated circuit (IC) withspecific programming can be used to implement a band-pass filter, suchas band-pass signal filter 116. Many such IC's are availablecommercially, for example from Texas Instruments, headquartered inDallas, Tex., or Analog Devices, headquartered in Norwood, Mass.Commercial front-end solutions are also available for the signalconversion units, such as signal conversion units 112, 113, and theband-pass signal filters 116, 117. The Active AFE5803 fully-integrated,8-channel ultrasound analog front end from Texas Instruments is one suchcommercial front end solution. Such devices can be programmed throughserial interface by sending specific device commands from a personalcomputing device, such as a personal computer or mobile device. Also, acustom application specific integrated circuit (ASIC) can be developedfor any one of or any combination of signal conversion units 112, 113and band-pass signal filters 116, 117.

After the amplified electrical analog signal is filtered by theband-pass signal filter 116, a filtered electrical analog signal istransferred from the band-pass signal filter 116 to theanalog-to-digital converter 118 (ADC) by a connection 122. Similar tothe connection 110 and the connection 120, the connection 122 can be awired or wireless connection, and the filtered electrical analog signalis conveyed to the analog-to-digital converter 118. The filteredelectrical analog signal can be transferred from the band-pass signalfilter to the analog-to-digital converter via a cloud-based storagemedium. In some embodiments, the analog-to-digital converter is ahigh-resolution, sigma-delta analog-to-digital convener. Any othersuitable, commercially available analog-to-digital converter can also beused in the embodiments of the present disclosure.

Still referring to FIG. 1, after the electrical analog signal passesthrough the signal conversion unit 112, a digital electrical signal iscommunicated through a connection 124 to a processing unit 126. Similarto the connections 110, 120, and 122, the connection 124 can be a wiredor wireless connection, or any combination thereof, and the digitalelectrical signal can be conveyed to the processing unit 126. Thedigital electrical signal can be transferred from the analog-to-digitalconverter 118 to the processing unit 126 via a cloud-based storagemedium. The processing unit 126, in the embodiment shown, includes asignal processor 128, a battery 130, and a physical memory 132. Theprocessing unit includes more or fewer units in other embodiments, andthe components of the processing unit are not required to be physicallycoupled or in close proximity and can exist as separate components.

In the embodiment shown, the signal processor 128 includes a personalcomputer (PC) used in combination with a digital signal processor (DSP)with a processor. Alternatively, a DSP can be used without a personalcomputer. The signal processor 128 is used to calculate approximateentropy, process, analyze, and classify acoustic signals, and provideresults regarding the characteristics of the MPF, including a flowregime of the MPF. More detail on these calculations is provided asfollows. In the embodiment shown, the signal processor 128 includes auser interface 134. Raw data, measurement results, characterizations ofthe MPF, and any other pertinent user input or signal processor outputinformation is input by and displayed to a user on the user interface134. One or more flow regimes of FIG. 5, described as follows, can beshown on the user interface 134.

The user interface 134 also is operable to accept user inputs to controlthe processing unit 126 and the system 100. The user interface, in someembodiments, includes audible and visual alerts, warnings, and alarmsresponsive to a MPF regime characterization. For example, if system 100determined that MPF was in a slug flow when a slug flow was notacceptable for system 100, an audible and visual warning is provided toa user.

The user interface 134 displays a graphical flow-type characterizationsuch as those shown in FIG. 5. The system 100 accepts user input by theuser interface 134 to change the flow regime, such as by controllingvalves, actuators, and other means in the pipeline 102. Alternatively,the system 100 can act independently, i.e. without contemporaneous userinput and based on pre-established rules and programs, to change theflow regime in the pipeline 102 if the current flow regime isunacceptable. Changing the MPF flow pattern is accomplished bycontrolling valves, actuators, and other means (not shown) in thepipeline 102. For example, if the system 100 determined that a MPF wasin a slug flow when a slug flow was not acceptable for system 100 basedon pre-established rules, the system 100 would act to change the MPFflow regime such as by controlling valves, actuators, and other means(not shown) in the pipeline 102.

The battery 130 is used to power the signal processor 128 and thephysical memory 132. In other embodiments, more or fewer batteries areused. Any raw data, calculations, measurement results, andcharacterizations of the MPF can be saved in the physical memory 132.The physical memory 132 is communicable with the signal processor 128,and further stores executable steps to calculate approximate entropy andrun a principal component analysis, both of which are discussed furtheras follows. The processing unit 126 can further be communicable with anoptional external database 136, which can include further executablesteps, programs, pre-determined values of MPF regimes, and any otherinput for or output of the processing unit 126.

In the embodiment of FIG. 1, the signal processor 128 is operable tocalculate approximate entropy of the MPF responsive to the receiveddigital signal. In statistics, approximate entropy (ApEn) is a techniqueused to quantify the amount of regularity and the unpredictability offluctuations over time-series data. ApEn was introduced by Steven Pincusin 1991 in “Approximate entropy as a measure of system complexity,”Proc. Natl. Acad. Sci. USA, Vol. 88, pp. 2297-2301, March 1991,Mathematics. This statistical method is used to quantify the complexityin noisy time series data. ApEn is robust and insensitive to artifactsor outliers, which means that infrequent, extremely small andinfrequent, extremely large values have a small effect on the ApEncalculation. ApEn assigns a positive real number to a sequence oftime-series of data, with larger values corresponding to greaterapparent process randomness or irregularity, and smaller valuescorresponding to instances of more recognizable patterns in the data.

Different multiphase flow patterns will have different values ofrandomness and flow complexity, especially over shorter time periods,and these values of randomness and flow complexity are approximatedusing the ApEn technique.

In the embodiment of FIG. 1, the signal processor 128 in combinationwith the physical memory 132 performs the following steps. First, thereceived digital signal is segmented into short term, medium term, andlong term time series. These time series from the digital signals areinputs for the ApEn algorithms described as follows. Next, for use inequations 1 and 2, positive real numbers are assigned to the timeseries, the positive real numbers including larger values and smallervalues, the larger values corresponding to process randomness, and thesmaller values corresponding to instances of recognizable patterns inthe digital signal. Next, certain positive real numbers are categorizedas outlier values. Following the preceding steps, using equations 1 and2, the short term, medium term, and long term approximate entropy valuesare calculated for the MPF based upon the short term, medium term, andlong term time series from the digital signal. For longer acousticemission measurements, multiple ApEn values are calculated to computeaverage values for short, medium, and long term ApEn.

The algorithm for calculating ApEn for time series data is known andprovided as follows. First, a time series of data would be obtained,such as from components 106 through 132 in FIG. 1. Such a time series ofdata can be represented by y(1), y(2), . . . , y(N), where N raw datavalues are measured at equally spaced time intervals. Next, an integerm, and a positive real number r are fixed, where m represents the lengthof the compared run of data, and r specifies the filtering level. Next,a sequence of vectors x(1), x(2), . . . , x(N−m+1) in R^(m) is formed,and real m-dimensional space is defined by x(i)=[y(i), y(i+1), . . . ,y(i+m−1)].

Following these steps, the sequence x(1), x(2), . . . , x(N−m+1) is usedto construct for each i, where 1≤i≤N−m+1,

${{C_{i}^{m}(r)} = \frac{{{number}\mspace{14mu}{of}\mspace{14mu}{x(j)}{such}\mspace{14mu}{that}\mspace{14mu}{d\left\lbrack {{x(i)},{x(j)}} \right\rbrack}} < r}{N - m + 1}},$where d[x, x′]=max_(a)|y(a)−y′(a)|.

The u(a) are the m scalar components of x. Here, d represents thedistance between the vectors x(i) and x(j), given by the maximumdifference in their respective scalar components.

Next, equation 1 is calculated using the elements described previously.Φ^(m)(r)=(N−m+1)⁻¹Σ_(i=1) ^(N-m+1) log(C _(i) ^(m)(r))  Eq. (1)

Following the previous steps, approximate entropy is calculatedaccording to equation 2.ApEn=Φ^(m)(r)−Φ^(m+1)(r)  Eq. (2)

For ApEn based MPF regime measurements and characterizations, a databaseof short, medium, and long term ApEn values that are representative ofthe respective MPF regime at various flow rates is developed. Such datacan be acquired from laboratory flow loops and actual field tests. Dataacquired from surface or downhole conditions can be used depending onthe target application. Different databases can be developed for systemsof different embodiments, such as the different embodiments of FIGS.1-4. Initial characterization of the systems of the present disclosurecan take place in a flow loop rather than a well or field application,but the flow loop data ideally should be representative of MPF regimeswithin a well or field application.

The MPF loop generally used in laboratory testing and qualification isclosed circuit (as there is a loop). Individual flow rates of water,brine, oil, and gas can be varied to generate representative flowconditions for different wells and fields. A separator is used within aflow loop, in some embodiments, to separate the individual phases. Theflow loops can also operate at greater temperatures and pressures, andthe actual oil field conditions can be replicated in the lab.

Following the calculation of the ApEn values from the digital signal,these calculated short term, medium term, and long term ApEn values arecompared to pre-calculated or pre-determined short term, medium term,and long term ApEn values in the physical memory 132 and, optionally,values in a similar database of pre-calculated values, such as theoptional external database 136. Finally, characteristics of the MPF aredetermined responsive to similarities between the short term, mediumterm, and long term approximate entropy values for the MPF calculatedfrom the digital signal and the pre-calculated short term, medium term,and long term approximate entropy values in the physical memory 132.After determination of the flow regime of the MPF, and othercharacteristics such as flow velocity (described further as follows withregard to FIG. 2), the calculations, results, and flow characterizationare displayed on the user interface 134, which accepts user inputs tocontrol the processing unit, and is operable to display thecharacteristics of the MPF to a user or operator of the system 100.

In the embodiments of the present disclosure, in order to characterizeMPF responsive to acoustic emission detection, a system is initiallycharacterized, optionally in a lab, to develop a detailed databasecontaining ApEn values for a variety of flow regimes. In certainembodiments of the present disclosure, in order to initiallycharacterize MPF in the system, the following steps for characterizationcan be carried out. Such steps enable the physical memory 132, or otherdatabases communicable with the processing unit 126, to have acomprehensive series of values that can be used to characterize MPF flowregimes. The steps, in one embodiment, proceed as follows.

First, at either or both a lab and a field site, different MPFconfigurations are generated in an applicable pipeline configuration.For the different MPF configurations that are generated, differentfractions of oil, water, gas, and solids should be used. Theseconditions, in some embodiments, are designed to substantially resembleexpected actual MPF configurations during field applications in whichMPF must be characterized according to systems and methods of thepresent disclosure. Next, a test matrix is defined where oil, water (orbrine), and gas flow rates are varied accordingly. These conditions, insome embodiments, are designed to substantially resemble expected actualMPF configurations during field applications in which MPF must becharacterized according to systems and methods of the presentdisclosure. Test matrices with the following conditions can begenerated: (1) stepped values of watercut; (2) stepped values of totalliquid flow; and (3) different MPF flow patterns, for example thoseshown in FIG. 5.

Following these steps, the ApEn is calculated for each of the testmatrix data points. The ApEn is calculated using the algorithms providedpreviously. In one embodiment, the following entropy values can becalculated: (1) short term (S): about 1-10 seconds; (2) medium term (M):about 10-120 seconds; and (3) long Term (L): about 2-10 minutes. Foreach particular flow regime, at each test point in the test matrix, theresulting ApEn calculated values can be in the form of a range of avalue, and not necessarily just a fixed number. By completing the stepsof an initial flow characterization, a data set of ApEn values fordifferent flow conditions is obtained and stored in a database such as,for example, the physical memory 132 in FIG. 1.

Referring now to FIG. 2, a schematic diagram of a correlation passiveacoustic emissions based system 200, according to one embodiment of thepresent disclosure, is shown. Certain elements shown in FIG. 2 are alsoshown in FIG. 1 and described previously. In the embodiment of FIG. 2,in addition to the acoustic emission sensor 106 mounted to the segment104 of the pipeline 102, a second acoustic emission sensor 107 ismounted to the segment 104 of the pipeline 102. As shown, the secondacoustic emission sensor 107 is downstream of the acoustic emissionsensor 106 in the Y1 direction; however, in other embodiments the secondacoustic emission sensor 107 can be disposed upstream of the acousticemission sensor 106 in the Y2 direction at a segment of the pipeline 102operable to support MPF.

In FIG. 2, a connection 111, a signal conversion unit 113, apreamplifier 115, a band-pass signal filter 117, an analog-to-digitalconverter 119, and connections 121, 123, and 125 are shown. These unitsare similar, respectively, to the connection 110, the signal conversionunit 112, the preamplifier 114, the band-pass signal filter 116, theanalog-to-digital converter 118, and the connections 120, 122, and 124described previously. In some embodiments, components 113, 115, 117,119, 121, 123, and 125 need not to be separate from components 112, 114,116, 118, 120, 122, and 124. For example, the connection 111 proceedingfrom the second acoustic emission sensor 107 can simply connect thesecond acoustic emission sensor 107 to the signal conversion unit 112and the other components.

In the embodiment of FIG. 2, the acoustic emission sensor 106 and thesecond acoustic emission sensor 107 are disposed a distance D apart.More than two acoustic emission sensors are used in other embodiments ofthe present disclosure. The pair of acoustic emission sensors 106, 107is implemented to measure the flow velocity through the pipe. Theacoustic emission sensors 106, 107 are placed relatively closely to oneanother such that the MPF pattern is coherent or similar as it passesboth sensors. The relative closeness of the acoustic emission sensors106, 107 to one another will vary depending, in part, on the geometry ofthe pipeline 102 and the physical properties of the liquid. The distancebetween the acoustic emission sensors 106, 107 can be about 1 meter, canbe about 0.5 meters, or can be about 5 meters. If more than two acousticemission sensors are used, the distance between the respective acousticemission sensors can be the same or can be different. If one sensor ison a vertical section of casing and another is on a horizontal sectionof casing. D will be the flow distance between both sensors, and not theactual distance.

As the MPF progresses through the segment 104 of the pipeline 102, bothof the acoustic emission sensors 106, 107 receive acoustic emissions ofthe MPF flow separated by the known distance D between them. If the MPFcharacteristics are coherent/substantially similar when measured at bothof the acoustic emission sensors 106, 107, then the time for the fluidto travel between the acoustic emission sensors 106, 107 is calculated.For example, at some time t₁ the MPF passes the acoustic emission sensor106 and at some time t₂, the MPF passes the second acoustic emissionsensor 107. If the MPF flow characteristics are substantially the sameat both points, a Δt is calculated to determine velocity with t₂−t₁=Δt.In certain embodiments, by correlating the received raw acousticemissions from both of the acoustic emission sensors 106, 107, anddetermining if both acoustic emissions are substantially the same, thenthe fluid velocity is provided by the equation velocity=D/Δt.

Therefore, the acoustic emission sensors 106, 107, when used in thisway, provide an accurate estimate for fluid velocity along with MPFregime measurement. The distance D should be carefully designed, becauseif D is large or very small, the acoustic emission sensors 106, 107 willnot see the same physical characteristics in the MPF regime, and thecorrelation between the acoustic emission sensors 106, 107 will notprovide reliable measurement results. The fluid velocity through thesegment 104 is calculated by the processing unit 126 and displayed onthe user interface 134. The user interface 134 displays the real-timefluid flow characteristics including fluid velocity and is operable todisplay graphs comparing past flow characteristics with current flowcharacteristics, including flow velocity.

Referring now to FIG. 3, a schematic diagram of an active acoustictransmission based system 300, according to one embodiment of thepresent disclosure, is shown. Certain components represented arenumbered according to the embodiment of FIG. 1 and represent the samecomponents described with reference to FIG. 1 previously. In theembodiment of FIG. 3, an acoustic transmitter 140 is mounted on thepipeline 102 proximate the segment 104 and is disposed substantially inline with an acoustic receiver 150. In the embodiments of FIGS. 3 and 4,the system configurations are “active.” In active system configurations,one or more acoustic transmitters are used to transmit an acousticsignal of a fixed frequency through the MPF, and the acoustic signal isreceived by one or more acoustic receivers. Subsequent processing isperformed on the received acoustic signal received by the acousticreceiver(s) to determine the MPF flow regime and other characteristics.

In some embodiments of the active systems described, the acoustictransmitters transmit an acoustic signal over a fixed acoustic frequencyor within a narrow, pre-set band of frequencies. This fixed frequency(or narrow, pre-set band of frequencies) is receivable by the acousticreceiver for one or more of storage, transmission, and processing. Theacoustic transmitter(s) and acoustic receiver(s) in the embodiments ofthe active systems have similar transducer properties including, but notlimited to, operating frequency, bandwidth, sensitivity, and beam angle.

In the embodiment of the system of FIG. 3 (and FIG. 4), the acoustictransmitter 140 and the acoustic receiver 150 are in direct contact withthe MPF. The acoustic transmitter 140 and the acoustic receiver 150 aremounted in fitted holes bored in the segment 104 of pipeline 102, suchthat a transmitting surface of the acoustic transmitter 140 and areceiving surface of the acoustic receiver 150 are in direct contactwith the MPF. The components 140, 150 are mounted in this configurationso that the acoustic energy effectively propagates from the acoustictransmitter 140 through the MPF to the acoustic receiver 150.

The acoustic transmitter 140 and the acoustic receiver 150 operate at afixed high frequency, in the range of about 0.5 MHz to about 2 MHz. Theacoustic transmitter 140 and the acoustic receiver 150 are operated at anarrow beam angle, in the range of about 5° to about 15°. In theembodiment of FIG. 3, the processing unit 126 further includes a signalgenerator unit 142 within signal processor 128. The signal generatorunit 142 is operable and controllable by the user interface 134 toprovide a drive signal 144 to the acoustic transmitter 140. In someembodiments, the signal generator unit generates a continuous sine wavesignal of a high frequency, in the range of about 0.5 MHz to about 2MHz. In other embodiments, however, other signals are generated by thesignal generator unit.

The drive signal for the acoustic transmitter 140, in the embodiment ofFIGS. 3 and 4, can be modified and can be a continuous sine wave or asquare wave with a frequency equal to the operating frequency of theacoustic transmitter 140 and the acoustic receiver 150. The amplitude ofthe signal is in the range of about 5 to about 10 volts (V). The drivesignal can be generated using a built-in oscillator in any commercialdigital signal processor (DSP) or microcontroller (MCU).

The drive signal 144 is amplified, in the embodiment shown, by anoperational amplifier 146 prior to being conveyed to the acoustictransmitter 140. In other embodiments, no operational amplifier isrequired. The operational amplifier 146 is a high-voltage amplifieroperable to about 50 volts (V) to about 100 V. The amplified signal isprovided to drive the acoustic transmitter 140. Once the acoustictransmitter 140 receives an amplified drive signal from the operationalamplifier 146, the acoustic transmitter 140 converts the electricaldrive signal to an acoustic signal 148. The acoustic signal 148 outputby the acoustic transmitter 140 can be a continuous signal. In someembodiments, the acoustic signal is of a high frequency, from about 0.5MHz to about 2 MHz.

The acoustic signal 148 from the acoustic transmitter 140 travelsthrough the MPF in the segment 104 of the pipeline 102 and is receivedby the acoustic receiver 150. The amplitude and energy of the receivedsignal at a particular time depends on the exact composition of the MPFat that time. The acoustic receiver 150 receives the acoustic signal 148and converts it to an electrical signal, in the embodiment shown anelectrical analog signal. Similar to as previously described with regardto FIG. 1, the received signal is preamplified by the preamplifier 114within the signal conversion unit 112. The received signal consists ofactual signal as well as acoustic noise from the flow or the pipeline.After the preamplifier 114, the signal passes through the band-passsignal filter 116, the analog-to-digital converter 118, and into theprocessing unit 126. The signal processor 128 computes the energy of thereceived signal (see equation 3 as follows) and calculate theapproximate entropy as described previously. Then, the signal processor128 analyzes and classifies the acoustic signals to provide and displaythe metering results of the MPF characteristics and flow regime.

The calculated results, along with raw data received by the acousticreceiver 150, is stored in the physical memory 132 and displayed usingthe user interface 134. Any data within the processing unit 126 can beconveyed, stored, and displayed at a remote location using any one of orany combination of wired and wireless communications such as wirelessinternet and Bluetooth technology.

Referring now to FIG. 4, a schematic diagram of a correlation activeacoustic transmission based system 400, according to one embodiment ofthe present disclosure, is shown. Certain elements shown in FIG. 4 arealso shown in FIGS. 1-3 and described previously. In the embodiment ofFIG. 4, in addition to the acoustic receiver 150 mounted to the segment104 of the pipeline 102, a second acoustic receiver 151 is mounted tothe segment 104 of the pipeline 102. As shown, the second acousticreceiver 151 is downstream of the acoustic receiver 150 in the Y1direction; however, in other embodiments the second acoustic receiver151 can be disposed upstream of the acoustic receiver 150 in the Y2direction at a segment of the pipeline 102 operable to support MPF.

In FIG. 4, a connection 111, a signal conversion unit 113, apreamplifier 115, a band-pass signal filter 117, an analog-to-digitalconverter 119, and connections 121, 123, and 125 are shown. These unitsare similar, respectively, to the connection 110, the signal conversionunit 112, the preamplifier 114, the band-pass signal filter 116, theanalog-to-digital converter 118, and the connections 120, 122, and 124described previously. In some embodiments, components 113, 115, 117,119, 121, 123, and 125 need not to be separate from components 112, 114,116, 118, 120, 122, and 124. For example, the connection 111 proceedingfrom the second acoustic receiver 151 can simply connect the secondacoustic receiver 151 to the signal conversion unit 112 and the othercomponents.

In the embodiment of FIG. 4, in addition to the acoustic transmitter 140mounted to the segment 104 of the pipeline 102, a second acoustictransmitter 141 is mounted to the segment 104 of the pipeline 102. Asshown, the second acoustic transmitter 141 is downstream of the acoustictransmitter 140 in the Y1 direction; however, in other embodiments thesecond acoustic transmitter 141 can be disposed upstream of the acoustictransmitter 140 in the Y2 direction at the segment of the pipeline 102operable to support MPF.

The acoustic transmitters 140, 141 and the acoustic receiver 150, 151operate at a fixed high frequency, in the range of about 0.5 MHz toabout 2 MHz. The acoustic transmitters 140, 141 and the acousticreceivers 150, 151 are operated at a narrow beam angle, in the range ofabout 5° to about 150. In the embodiment of FIG. 4, the processing unit126 further includes a signal generator unit 142 within the signalprocessor 128. The signal generator unit 142 is operable andcontrollable by the user interface 134 to provide a drive signal 144 tothe acoustic transmitters 140, 141. The signal generator unit 142generates a continuous sine wave signal of a high frequency, in therange of about 0.5 MHz to about 2 MHz. In other embodiments, however,other signals can be generated by the signal generator unit.

The drive signal 144 is amplified by the operational amplifier 146 and asecond operational amplifier 147 prior to being conveyed to the acoustictransmitters 140, 141, respectively. In other embodiments, nooperational amplifiers are required. The operational amplifiers 146, 147are high-voltage amplifiers operable to about 50 V to about 100 V. Theamplified signal is provided to drive the acoustic transmitters 140,141. Once the acoustic transmitters 140, 141 have received an amplifieddrive signal from the operational amplifiers 146, 147, respectively, theacoustic transmitters 140, 141 convert the electrical signal to acousticsignals 148, 149. The acoustic signals 148, 149 output by the acoustictransmitters 140, 141 are continuous signals. In some embodiments, theacoustic signals are of a high frequency, from about 0.5 MHz to about 2MHz.

The acoustic signals 148, 149 from the acoustic transmitters 140, 141travel through the MPF in the segment 104 of the pipeline 102 and arereceived by the acoustic receivers 150, 151, respectively. The amplitudeand energy of the received signals at a particular time depends on theexact composition of the MPF at that time. The acoustic receivers 150,151 receive the acoustic signals 148, 149 and convert them to anelectrical analog signal. Similar to as previously described with regardto FIG. 1, the received signals are preamplified by the preamplifiers114, 115 within the signal conversion units 112, 113, respectively. Thereceived signals consist of actual, useful signal as well as acousticnoise from the MPF, the pipeline, and the environment surrounding thepipeline. After the preamplifiers 114, 115, the signals pass through theband-pass signal filters 116, 117, the analog-to-digital converters 118,119, and into the processing unit 126. The signal processor 128 computesthe energy of the received signals and calculate the approximate entropyas described previously. Then, the signal processor 128 analyzes andclassifies the acoustic signals to provide and display the meteringresults of the MPF characteristics and flow regime.

The calculated results, along with raw data received by the acousticreceivers 150, 151 are stored in the physical memory 132 and displayedusing the user interface 134. Any data within the processing unit 126can be conveyed, stored, and displayed at a remote location using anyone of or any combination of wired or wireless communications such aswireless internet and Bluetooth technology.

In the embodiment of FIG. 4, the acoustic receiver 150 and the secondacoustic receiver 151 are disposed a distance D apart. More than twoacoustic receivers can be used in other embodiments of the presentdisclosure. A pair of acoustic receivers 150, 151 is implemented tomeasure the flow velocity through the pipe. The acoustic receivers 150,151 are placed relatively closely to one another such that the MPFpattern is coherent or similar as it passes both sensors. The relativecloseness of the acoustic receivers 150, 151 to one another will varydepending, in part, on the geometry of the pipeline 102 and the physicalproperties of the liquid. The distance D between the acoustic receivers150, 151 can be about 1 meter, or about 0.5 meters, or about 5 meters.If one sensor is on a vertical section of casing and another is on ahorizontal section of casing, D will be the flow distance between bothsensors, and not the actual distance.

As the MPF progresses through the segment 104 of the pipeline 102, bothof the acoustic receivers 150, 151 receive the acoustic signals 148, 149from the acoustic transmitters 140, 141, respectively, through the MPFflow. The acoustic receivers 150, 151 are separated by the knowndistance D between. If the MPF characteristics arecoherent/substantially similar when measured at both of the acousticreceivers 150, 151, then the time for the fluid to travel between theacoustic receivers 150, 151 is calculated. For example, at some time t₁the MPF passes the acoustic receiver 150 and at some time t₂, the MPFpasses the second acoustic receiver 151. If the MPF flow characteristicsare substantially the same at both points, a Δt is calculated todetermine velocity as t₂−t₁=Δt. In certain embodiments, by correlatingthe received raw signals at both of the acoustic receivers 150, 151 fromthe acoustic transmitters 140, 141, respectively, and determining ifboth signals are substantially the same, then the fluid velocity isprovided by the equation velocity=D/Δt.

Therefore, the acoustic receivers 150, 151, when used in this way,provide an accurate estimate for fluid velocity along with MPF regimemeasurement. The distance D should be carefully designed, because if Dis large or very small, the acoustic receivers 150, 151 will not see thesame physical characteristics in the MPF regime, and the correlationbetween the acoustic receivers 150, 151 will not provide reliablemeasurement results. The fluid velocity through the segment 104 iscalculated by the processing unit 126 and displayed on the userinterface 134.

Referring now to FIGS. 3 and 4, for the active acoustic transmissionbased system 300 and the correlation active acoustic transmission basedsystem 400, the following steps are used, in one embodiment, to operatethe systems in such a way as to accurately measure, calculate, anddetermine the characteristics and flow regime of a MPF. First, theacoustic transmitter 140 is activated with the acoustic receiver 150. Inthe embodiment of FIG. 4, the second acoustic transmitter 141 and thesecond acoustic receiver 151 would also be activated. Next, the acousticsignal received by the acoustic receiver 150 is pre-amplified by thepreamplifier 114, filtered by the band-pass signal filter 116 andconverted into a digital signal by the analog-to-digital converter 118.In the embodiment of FIG. 4, the acoustic signal received by the secondacoustic receiver 151 is pre-amplified by the preamplifier 115, filteredby the band-pass signal filter 117 and converted into a digital signalby the analog-to-digital converter 119.

Next, the digital signal is acquired by the processing unit 126 and thesignal processor 128. The signal processor 128 can calculate the energy.E_(s), of a received acoustic signal, x(n), using equation 3, shown asfollows:E _(S)=Σ_(−∞) ^(∞) |x(n)|²  Eq. (3)

For a finite number of samples N, equation 3 can be re-written asequation 3a:E _(S)=Σ_(n=0) ^(N) |x(n)|²  Eq. (3a)

In some embodiments, energy is computed for every 1000 cycles ofreceived signal. For example, if the systems 300 and 400, including theacoustic transmitter(s) and the acoustic receiver(s), are operating at 1MHz, energy will be computed for every 1000 cycles, which is equal to1000 energy measurements per second. Using this example, if the receivedsignal is sampled at a sampling frequency f_(s), the number of samples Nwill be equal to equations 3b and 3c shown as:

$\begin{matrix}{N = {\frac{1}{{acoustic}\mspace{14mu}{freqeuncy}} \times f_{s} \times {Number}\mspace{14mu}{of}\mspace{14mu}{cycles}}} & {{Eq}.\mspace{14mu}\left( {3b} \right)} \\{N = {\frac{1}{10^{6}} \times f_{s} \times 1000\;{{Eq}.}}} & \left( {3c} \right)\end{matrix}$

The signal processor 128 segments the acoustic energy signal(s) intoshort, medium, and long term time series. These time series signals willbe inputs for the ApEn algorithm, as described previously with regard toFIG. 1. ApEn values are calculated for each time series. For longeracoustic emission measurements, multiple ApEn values will be calculatedto compute average values for short, medium, and long term ApEn. Thecomputed values of short, medium, and long term ApEn can be searched inthe corresponding physical memory 132 and optional external database(s),such as the optional external database 136, which include pre-calculated(pre-determined) ApEn values to provide the MPF measurement result. Inthe embodiment of FIG. 4, cross correlating acoustictransmitter/receiver pairs 140, 150 and 141, 151 provide an estimate forfluid velocity through the segment 104 and the pipeline 102.

Referring now to the systems of FIGS. 3-4, in certain embodiments, unitssuch as the acoustic receivers 150, 151 and the acoustic transmitters140, 141 can be clamped on the outside of a pipeline, such as thepipeline 102, using a couplant between the units' surfaces and thepipeline 102. Clamping components only on the outside of the pipelineresults in a non-invasive system. In such a system, some of the acousticsignals will propagate through the MPF, while some of the acousticsignals will propagate through the pipeline. The portion of acousticsignal that propagates through the pipeline is referred to as the lambwave. Signal processing techniques are required to separate the lambwaves from useful signal travelling through the MPF. These signalprocessing techniques are executable on the signal processor 128. Theseparated acoustic signal is processed using the same ApEn techniques asdescribed earlier.

In some embodiments, passive systems of the present disclosure, such asthose shown in FIGS. 1 and 2 and described previously, use acousticemission sensors mounted on the outside or exterior of a pipe to createa non-invasive characterization system, optionally with the use of acouplant. In some embodiments, active systems of the present disclosure,such as those shown in FIGS. 3 and 4 and described previously, useacoustic transmitters and acoustic receivers that are in direct contactwith the MPF. The acoustic transmitters and acoustic receivers aremounted in fitted holes bored in a portion of a pipeline, such that atransmitting surface of the acoustic transmitters and a receivingsurface of the acoustic receivers are in direct contact with the MPF. Insome embodiments, the components are mounted in this configuration sothat the acoustic energy effectively propagates from the acoustictransmitters through the MPF to the acoustic receivers.

In some embodiments, if the acoustic transmitter and acoustic receiverunits are mounted to be in direct contact with the MPF, the processingof the received signal will be computationally less expansive, as thereis no lamb wave received by the acoustic receiver. In some embodiments,if the acoustic transmitter and acoustic receiver units are mountedexterior to the pipe, a significant portion of the transmitted acousticsignals will propagate through the pipeline wall to the acousticreceiver without travelling through the MPF (lamb waves). Signalprocessing techniques are required to separate the lamb waves from thereceived signal that has traveled through the MPF, which may result inmore complex signal processing and computation. The advantage of such asystem, however, is the non-invasive nature of the acoustic transmitterand acoustic receiver units, as they can be deployed anywhere on apipeline just by clamping the acoustic transmitter and acoustic receiverunits on the pipeline along with all other electronics and processingunits external to the pipeline.

The undesirable lamb waves can be treated as a continuous noise signal,because a continuous acoustic signal is transmitted by the acoustictransmitter(s). The lamb wave noise signal will have the same frequencyas the actual signal, but a different amplitude and phase, depending inpart on the pipe wall through which it is travelling. This lamb wavenoise signal can be characterized (for example in the laboratory) andremoved from the received signal in the processing unit, in theband-pass signal filter, or by the acoustic receiver by setting aspecific frequency.

The systems of FIGS. 1-4 can be deployed on surface pipelines andfacilities. The systems can be deployed on wellheads and can also bedeployed downhole, inside a wellbore for MPF regime measurement fromwellbores. The systems can be a part of a permanent smart completion, ora retrievable system in mother bore or laterals. Permanent smartcompletions incorporate permanent downhole sensors, flow measurementdevices, and surface-controlled downhole flow control valves, enablingthe monitoring, evaluation, and active production (or injection)management in real-time without any well interventions. Data aretransmitted to the surface for local or remote monitoring. These systemsare permanently deployed for the complete life of a well.

A retrievable system is a wireline or coiled tubing deployable systemthat is lowered into the well for a limited amount of time to performspecific measurements or logging operations. The system is retrievablefrom the well once the desired operation has been performed.

In some embodiments of the systems of FIGS. 1-4, the systems can beintegrated with one or more sensor measurements (including single ordifferential pressure measurements) to improve the accuracy of data andMPF calculations. Additionally, any one of or any combination oftemperature sensors, pressure sensors, accelerometers, densimeters, andflow meters is contemplated for use in the systems and methods of thepresent disclosure. Measurements from such devices are provided by wiredor wireless means to the processing unit 126 and displayed on the userinterface 134.

In certain embodiments of the systems of FIGS. 1-4, the electronic unitssuch as, for example, the acoustic emission sensor 106, the preamplifier114, the band-pass signal filter 116, the analog-to-digital converter118, the processing unit 126, the signal processor 128, the battery 130,the physical memory 132, and the user interface 134 and any suitable,required processing circuitry of the systems can be combined in a singlesystem responsive to Application Specific Integrated Circuit (ASIC) andSystem-on-Chip (SoC) methodologies. In these embodiments, for example, avery compact system can be developed. Such a system with properprotective packaging would be suitable for harsh environments faced indownhole deployments inside a well.

Referring now to FIG. 5, a graphical representation is shown of MPFregimes, optionally for display on the user interface 134 in anembodiment of the present disclosure. While other flow regimes aremeasured and characterized by the systems of FIGS. 1-4, FIG. 5 providescertain flow graphics that are useful to an operator of the systems.Bubble flow is characterized by small gas bubbles flowing along the topof the pipe. Elongated bubble flow is characterized by collisionsbetween the individual bubbles occurring more frequently with increasinggas flow rate and coalescing into elongated “plugs.” This is oftencalled plug flow. Stratified smooth flow is characterized by gas plugscoalescing to produce a continuous gas flow along the top of the pipewith a smooth gas-liquid interface typical of stratified flow atrelatively low flow rates. Stratified wavy flow is characterized by thegas-liquid interface being rarely smooth with ripples appearing on theliquid surface. In this embodiment, the amplitude increases withincreased gas flow rate.

Slug flow is characterized by the amplitude of the waves travellingalong the liquid surface becoming sufficiently large enough for them tobridge the top of the pipe, and thus the flow enters the slug flowregime. In this embodiment, the gas flows as intermittent slugs withsmaller bubbles entrained in the liquid. Annular flow is characterizedby the gas flow rate being large enough to support the liquid filmaround the pipe walls. Liquid is also transported as dropletsdistributed throughout the continuous gas stream flowing in the centerof the pipe. The liquid film is thicker along the bottom of the pipebecause of the effect of gravity.

As noted previously, acoustic emissions from MPF are dependent upon, inpart, gas bubble formation and cavitation, regime breakage andcoalescence, and interaction of various phases within a multiphase flow.These characteristics vary for different MPF regimes, flow rates, andalso for different relative amounts of liquid, gas/vapor, and solids inthe MPF. The systems and methods of the present disclosure surprisinglyand unexpectedly are able to accurately and efficiently characterizeflow regimes, such as, for example, those in FIG. 5, by applying theApEn calculations to remove unwanted acoustic noise and outliermeasurements, which have prevented the accuracy of such systems andmethods.

In other embodiments of the systems of FIGS. 1 and 2, principalcomponent analysis (PCA) is applied to optimize the systems. The systemsof FIGS. 1 and 2 are considered passive systems, at least in partbecause there are no added acoustic transmitters. The acoustic emissionsreceived by the acoustic emission sensors 106, 107 would arise largelyfrom the MPF, the pipeline 102, and the local environment surroundingthe acoustic emission sensors 106, 107. Initially, in the systems ofFIGS. 1 and 2, the acoustic emission sensors 106, 107 can be broadbandacoustic emission sensors operable to gather data including broadbandfrequency emissions from the MPF, the pipeline 102, and the surroundingenvironment. The data acquired by such broadband acoustic emissionsensors is gathered in any one of or any combination of a fieldapplication, such as in-situ in a well or with a pipeline, and in a labsetting in a closed flow loop with tight controls on flowcharacteristics. In one embodiment, while such data is being gathered,the appropriate Reynolds number is known for the MPF for which data isbeing gathered.

With the data gathered, Lime series of acoustic waveforms are formed,and by performing a Fourier Transformation on the data, the data isconverted into measurements of acoustic power as a function offrequency. After the Fourier transformation, a suite of measurements isperformed using a test matrix including different conditions of the MPFincluding, but not limited to, stepped values of watercut, steppedvalues of total liquid flow, and different multiphase flow patterns,such as, for example, those shown in FIG. 5. Once a full dataset hasbeen gathered, the data is post processed using PCA on the dataset. PCAis a multivariate method that is optimal for handling co-linearity, andis described in detail in Nørgaard, L. et al., “Principal ComponentAnalysis and Near Infrared Spectroscopy,” A white paper from FOSS. Inorder to calculate a PCA model, several different algorithmic approachescan be applied. Several of these are implemented in commercial softwarepackages that offer the possibility to both calculate and presentresults from a PCA model.

In other words, PCA makes it possible to determine what variables aremost important for accurately determining a system characteristic. Apipeline, such as the pipeline 102, will emit various acoustic emissionsat various frequencies over time, especially if there is a MPFproceeding through the pipeline 102. The acoustic emission sensor, suchas acoustic emission sensor 106, receives and records all of theseacoustic emissions in situations in which the acoustic emission sensoris a broadband acoustic emission sensor. In some embodiments of thesystems and methods described, however, not all acoustic emissionsreceived by the acoustic emission sensor(s) are pertinent to thedetermination of MPF characteristics and flow regime. For example,certain frequencies change in a system, but the flow characteristics andthe flow regime of a MPF may not. In such a case, the systems andmethods of the present disclosure would not want to interpret such achange in frequency as having an effect on MPF characteristics or theMPF regime.

Therefore, once a full dataset has been gathered from one or morebroadband acoustic emission sensors, the dataset is post-processed byperforming PCA on the dataset. PCA will, in some embodiments, identifythe linear combination of key frequencies which are used to approximatea score which correlates or clusters resulting values into separableclassifications. In turn, this will also identify which frequencies areless significant than others. Furthermore, such a process of PCA allowsfor certain hardware optimization and cost savings. For example,broadband acoustic emission sensors can be replaced with an array ofless expensive single frequency tuned receivers where the frequenciesare in line with the values output from the PCA analysis. This canreduce system cost and computational overhead, because the tunedreceivers are acting as band-pass signal filters which are only lookingat the significant points of the measurement.

In order to calculate a PCA model, several different algorithmicapproaches can be applied. Several of these are implemented incommercial software packages that offer the possibility to bothcalculate and present results from a PCA model. In other embodiments, asystem can be implemented with a broadband receiver, and then a seriesof parallel band-pass signal filters can be implemented in hardware orusing digital signal processing techniques, tuned towards thesignificant frequencies identified by the PCA approach.

Referring now to FIG. 6, a flow chart for an embodiment for a method ofoperation of the system of FIG. 4, is provided. Similar methods ofoperations can be used in other embodiments of the systems presented.Starting at step 600, one or more users or operators of the system 400can be prompted by the user interface 134 to start the system 400. Sucha prompt can include audible and visual prompting, and the user oroperator can be required to affirmatively start the system, or thesystem can start without an affirmative response from the user oroperator. A suitable affirmative response from a user or operator mightinclude actuating any one of or any combination of a button, switch, andlever, touching a touchscreen, and responding by voice to a userinterface operable to accept voice commands. The user interface 134 andthe processing unit 126 are operable to operate with remote devices suchas, for example, laptop computers and smart phones through wired orwireless networks, devices, or communication schemes, as will beunderstood by those skilled in the art. Thus, the selections made by auser or operator described can be made remotely.

At step 600, the flow through the pipeline 102 can already be engaged,and can optionally already be flowing in a MPF regime. In someembodiments, however, the flow through the pipeline need not be engagedwhen the system is started and the flow through the pipeline need not beengaged at the same time the system is started. The flow through thepipeline 102 can be stopped, started, increased, or decreased at anypoint in the method of operation of FIG. 6 through any one of orcombination of valves, stops, actuators, and similar devices known inthe art. The flow chart for the method of operation shown in FIG. 6, insome embodiments, is completely automated within the processing unit126, and step 600 for starting the system 400 arises responsive to theconditions of the system 400, without contemporaneous user input.

At step 602, the user interface 134 can prompt the user or operator toactivate any one of or both of the acoustic receiver 150 and the secondacoustic receiver 151. At step 604, the user interface 134 can promptthe user or operator to activate any one of or both of the acoustictransmitter 140, and the second acoustic transmitter 141. In certainembodiments of the system 400, the selection for one or more acousticreceivers and one or more acoustic transmitters is made by the system400 responsive to system characteristics, such as, for example, thepipeline and system design, operating conditions, flow conditions, andpast recorded measurements of the system 400.

At step 606, the system 400 operates to determine if one or more of theacoustic transmitters 140, 141 have been activated. If the system 400determines that one or more of the acoustic transmitters 140, 141 havebeen activated, then at step 608 the system provides the user oroperator the opportunity to select or modify the drive signal 144. Thesignal generator unit 142 is operable and controllable by the userinterface 134 to provide a drive signal 144 to one or both of theacoustic transmitters 140, 141. In some embodiments, the signalgenerator unit generates a continuous sine wave signal of a highfrequency, for example in the range of about 0.5 MHz to about 2 MHz. Inother embodiments, however, other signals at other frequencies aregenerated by the signal generator unit after selection by a user oroperator on the user interface.

At step 610, the system 400 begins to collect data at the one or moreacoustic receivers that have been activated by the user. It is to benoted that the acoustic receivers 150, 151 can be operable to receivefrequencies from only the acoustic transmitters 140, 141, from only theMPF, or from both the acoustic transmitters 140, 141 and the MPF. Thiscan be done using any one of or combination of cut-off frequencies andset-frequency band-pass signal filters.

After the one or more acoustic receivers 150, 151 begin collecting datathrough one or more acoustic signals, the one or more signals ispreamplified at step 612, filtered at step 614, and converted from ananalog electrical signal to a digital signal at step 616. These stepsare optionally carried out in the units of the signal conversion units112, 113, described previously.

At step 618, the digital signal is processed in the processing unit 126,which includes the signal processor 128 and the physical memory 132. Thedigital signal is processed to calculate the approximate entropy ofshort term, medium term, and long term data series collected from anyone of or any combination of the acoustic receivers 150 and 151. At step620, the signal processor 128 determines if the approximate entropycalculations are recognized or comparable to pre-calculated orpre-determined approximate entropy values contained in any one of or anycombination of databases and the physical memory 132. If the ApEn valuesof the measured signal are not recognizable, the system will continue tocollect data at the acoustic receivers 150, 151.

At step 622, the system determines if there is greater than one acousticreceiver activated with the greater than one acoustic receiver receivingcoherent data, or data showing that a MPF with similar characteristicsis passing by the greater than one acoustic receivers. If this is nottrue, then the user or operator is given the option to once again selectthe acoustic receiver before the system calculates flow velocity at step624. If there is more than one operating acoustic receiver, and the databeing retrieved at both sensors is coherent, then the flow velocitywithin the segment 104 of the pipeline 102 is calculated at step 624.

Next at step 626, the system 400 determines the flow regime in responseto the ApEn calculations and the flow velocity. In other embodiments,the flow velocity is not needed and is not used in determining the flowregime, such as, for example, in the embodiment of the system 100 inFIG. 1 and the system 300 in FIG. 3. The flow regime can be determinedand approximated to one of the flow regimes shown in FIG. 5 anddescribed previously, or can be determined and approximated to acombination of any one of these regimes.

In other embodiments, the flow regime is determined to be some otherflow regime responsive to the acoustic signals received by the acousticreceivers. After the flow regime is determined, the flow characteristicsare displayed to a user or operator by the user interface 134 at step628. While in the embodiments of FIGS. 1-4 the user interface 134 isshown to be a component of the processing unit 126, the user interface134 can be a separate unit coupled by wired or wireless communication tothe processing unit 126, such as a smart phone or laptop computer.

Thus, a user or operator can receive displayed flow characteristicsremotely and in real-time, separate from the system 400. The user oroperator also can control the systems remotely by input into the userinterface 134. At step 628, other real-time data is also displayed onthe user interface 134 not calculated in the system 400, but obtainedfrom other meters and means such as, for example, flow meters, thermalmeasurement devices, densimeters, accelerometers, and similar devicesknown in the art.

In the various embodiments of the disclosure described, a person havingordinary skill in the art will recognize that various types of memoryare readable by a computer, such as the memory described in reference tothe various computers and servers, e.g., computer, computer server, webserver, or other computers with embodiments of the present disclosure.

The singular forms “a,” “an,” and “the” include plural referents, unlessthe context clearly dictates otherwise.

Examples of computer-readable medium can include but are not limited to:one or more nonvolatile, hard-coded type media, such as read onlymemories (ROMs), CD-ROMs, and DVD-ROMs, or erasable, electricallyprogrammable read only memories (EEPROMs); recordable type media, suchas floppy disks, hard disk drives, CD-R/RWs, DVD-RAMs, DVD-R/RWs,DVD+R/RWs, flash drives, memory sticks, and other newer types ofmemories: and transmission type media such as digital and analogcommunication links. For example, such media can include operatinginstructions, as well as instructions related to the systems and themethod steps described previously and can operate on a computer. It willbe understood by those skilled in the art that such media can be atother locations instead of, or in addition to, the locations describedto store computer program products, e.g., including software thereon. Itwill be understood by those skilled in the art that the various softwaremodules or electronic components described previously can be implementedand maintained by electronic hardware, software, or a combination of thetwo, and that such embodiments are contemplated by embodiments of thepresent disclosure.

In the drawings and specification, there have been disclosed embodimentsof methods, systems, and non-transitory computer-readable medium havingstored computer programs of the present disclosure, and althoughspecific terms are employed, the terms are used in a descriptive senseonly and not for purposes of limitation. The embodiments of methods,systems, and non-transitory computer-readable medium having storedcomputer programs of the present disclosure have been described inconsiderable detail with specific reference to these illustratedembodiments. It will be apparent, however, that various modificationsand changes can be made within the spirit and scope of the embodimentsof methods, systems, and non-transitory computer-readable medium havingstored computer programs of the present disclosure as described in theforegoing specification, and such modifications and changes are to beconsidered equivalents and part of this disclosure.

That claimed is:
 1. A method for characterizing multiphase fluid flow(MPF) to enhance measuring and monitoring of a flow regime in a segmentof pipe for hydrocarbon-production operations, the method comprising thesteps of: sensing an acoustic emission from a MPF, the segment of pipeoperable to support the MPF in hydrocarbon-production operationsincluding at least two physical phases; converting the acoustic emissionto an electrical signal; segmenting the electrical signal into shortterm, medium term, and long term time series; assigning positive realnumbers to the time series, the positive real numbers including largervalues and smaller values, the larger values corresponding to processrandomness, and the smaller values corresponding to instances ofrecognizable patterns in the electrical signal; categorizing certainpositive real numbers as outlier values; calculating short term, mediumterm, and long term approximate entropy values for the MPF responsive tothe short term, medium term, and long term time series from theelectrical signal by performing the following steps: assigning each ofthe short term, medium term, and long term time series of data tovariables each representing raw data values measured at equally spacedtime intervals, forming a sequence of vectors using the variables eachrepresenting raw data values, using the sequence of vectors to constructscalar components representing the distance between each respectivevector sequence given by the maximum difference in their respectivescalar components, calculating each of the short term, medium term, andlong term time series approximate entropy values using the scalarcomponents, comparing the short term, medium term, and long termapproximate entropy values for the MPF to pre-determined short term,medium term, and long term approximate entropy values; and determiningcharacteristics of the MPF responsive to similarities between the shortterm, medium term, and long term approximate entropy values for the MPFand the pre-determined short term, medium term, and long termapproximate entropy values.
 2. The method of claim 1, further comprisingthe step of displaying the characteristics of the MPF on a userinterface, where the user interface is operable to graphically representat least one flow regime.
 3. The method of claim 1, further comprisingthe step of preamplifying the electrical signal before the step ofsegmenting the electrical signal.
 4. The method of claim 3 furthercomprising the step of filtering the electrical signal, beforesegmenting the electrical signal, responsive to programmed cutofffrequencies in a band-pass signal filter derived from an operatingfrequency and bandwidth of an acoustic emission sensor.
 5. The method ofclaim 4 further comprising the step of converting the electrical signalto a digital signal, before segmenting the electrical signal.
 6. Themethod of claim 1, where the step of sensing an acoustic emissioncomprises the step of sensing a first acoustic emission, and furthercomprises the step of sensing a second acoustic emission from the MPF,the second acoustic emission being sensed simultaneously with and at adistance D from the first acoustic emission.
 7. The method of claim 6,further comprising the step of calculating an accurate measurement offlow velocity of the MPF in response to the distance D and sensing thefirst acoustic emission and sensing the second acoustic emission.
 8. Themethod of claim 1, further comprising the step of conducting a principalcomponent analysis, where the principal component analysis comprises thesteps of: gathering acoustic emission data under a variety of flowparameters in situations in which an appropriate Reynolds number isknown for the MPF for which data is being gathered; forming time seriesof acoustic waveforms; performing a Fourier Transformation on the data,the data being converted into measurements of acoustic power as afunction of frequency; executing a suite of measurements using a testmatrix including different conditions of the MPF including at least onevariable selected from the group consisting of: stepped values ofwatercut, stepped values of total liquid flow, and multiphase flowpatterns; and post-processing the data by applying principal componentanalysis to the data to determine measurable frequencies relevant todetermining the characteristics of the MPF.
 9. The method of claim 8further comprising the step of optimizing the step of sensing anacoustic emission from the MPF to receive the frequencies determined bythe principal component analysis to be relevant to determining thecharacteristics of the MPF.
 10. The method of claim 1, furthercomprising the step of conducting a principal component analysis, wherethe principal component analysis comprises the steps of: gatheringacoustic emission data under a variety of flow parameters in situationsin which an appropriate Reynolds number is known for the MPF for whichdata is being gathered; forming time series of acoustic waveforms;performing a Fourier Transformation on the data, the data beingconverted into measurements of acoustic power as a function offrequency; executing a suite of measurements using a test matrixincluding different conditions of the MPF including at least onevariable selected from the group consisting of: stepped values ofwatercut, stepped values of total liquid flow, and multiphase flowpatterns; and post-processing the data by applying principal componentanalysis to the data to determine measurable frequencies relevant todetermining the characteristics of the MPF.
 11. The method of claim 10further comprising the step of optimizing the step of sensing anacoustic emission from the MPF to receive the frequencies determined bythe principal component analysis to be relevant to determining thecharacteristics of the MPF.